Decide what to test first.
Structure the six most common explanations for an operating problem, mark what the evidence actually supports, and leave with a falsifiable first test—not a software-generated claim that the root cause has been found.
- Operating hypotheses
- 6
- Evidence inputs
- 24
- Prioritized test sequence
- 1
- No signup
- Evidence-weighted triage
- Explicit optional submission
Working dataAssessment selections are evaluated in the current page. Nothing is submitted unless the visitor explicitly sends the optional scenario and contact form.
Product boundaryThis self-assessment ranks hypotheses and proposes a falsifier; it does not diagnose a root cause or replace direct observation and source-system evidence.
Build the evidence pattern before naming the constraint.
Rate the strength of the evidence, mark the observations already supported, and use the resulting sequence to design the smallest useful falsification test.
Rate the strength of evidence already available, then mark only the observations you can support. The output is a test-priority map—not a probability or root-cause verdict.
Add evidence strength or a supported observation to create the first test sequence.
- 1Demand signal0
- 2Effective capacity0
- 3Quality and rework0
- 4Sequencing and flow0
- 5Decision rights0
- 6Measurement integrity0
Published scoring: 40% stated evidence strength + 60% supported observations. The score ranks what to test first; it does not estimate causal probability. An engagement validates the hypothesis against operating data before action.
Challenge the evidence
Ask which observation is measured, which is inferred, and which would be contested by the frontline or finance.
Run the falsifier
Test the leading hypothesis against the condition that would weaken it before allocating resources.
Model the decision
If the hypothesis survives, use the Recovery Model to quantify the capacity or timing consequence.